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Random Regression Coefficient Models
2020This chapter describes a modification of the nested error regression model having random regression coefficients. We can intuitively expect that the slope parameters of some explanatory variable are not constant and therefore they should take different values in different domains.
Domingo Morales +3 more
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Random regression coefficients
1994Abstract In Chapter 2 we studied the analysis of covariance with random effects as a method for the description of between-cluster differences.
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Regression with random coefficients
Omega, 1978Abstract The first half of the seventies has seen a virtual explosion of interest in the use of random coefficient regression techniques which, for the most part, have been developed or refined by econometricians. However, given the importance of regression analysis as a managerial tool, it is surprising to find scant mention of random coefficient ...
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Random sliced inverse regression
Communications in Statistics - Simulation and Computation, 2015ABSTRACTSliced Inverse Regression (SIR; 1991) is a dimension reduction method for reducing the dimension of the predictors without losing regression information. The implementation of SIR requires inverting the covariance matrix of the predictors—which has hindered its use to analyze high-dimensional data where the number of predictors exceed the ...
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Journal of the American Statistical Association, 1969
Abstract The use of observations of a random function in space (random field) as independent variables in regression is considered including the numerical aspects. Details are presented for obtaining a numerical approximation to a Karhunen-Loeve expansion when the random function is observed at a large number of points.
Ayala Cohen, Richard H. Jones
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Abstract The use of observations of a random function in space (random field) as independent variables in regression is considered including the numerical aspects. Details are presented for obtaining a numerical approximation to a Karhunen-Loeve expansion when the random function is observed at a large number of points.
Ayala Cohen, Richard H. Jones
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Prediction in Random Coefficient Regression Models
Biometrical Journal, 1990AbstractMuch attention has been given to the problem of predicting future observations for some individual within a random coefficient regression (RCR) model, using the previous observations on that individual as well as the information from the rest of the data material.
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Regression analysis and random sampling
Journal of Statistical Planning and Inference, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Semiparametric random coefficient regression models
Annals of the Institute of Statistical Mathematics, 1993zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Estimation of random regression parameters
Biometrical Journal, 1980AbstractThe problem of the best linear unbiased estimation (BLUE) of random regression parameters is considered. It is proved that increasing informations about the mean value of the parameters both extend the class of estimable linear functionals and improve on the estimation. In all investigated cases the uniqueness of BLUE is proved.
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Random Oracles for Regression Ensembles
2011This paper considers the use of Random Oracles in Ensembles for regression tasks. A Random Oracle model (Kuncheva and Rodriguez, 2007) consists of a pair of models and a fixed randomly created “oracle” (in the case of the Linear Random Oracle, it is a hyperplane that divides the dataset in two during training and, once the ensemble is trained, decides ...
Carlos Pardo +3 more
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